108,148 research outputs found
Extend Commitment Protocols with Temporal Regulations: Why and How
The proposal of Elisa Marengo's thesis is to extend commitment protocols to
explicitly account for temporal regulations. This extension will satisfy two
needs: (1) it will allow representing, in a flexible and modular way, temporal
regulations with a normative force, posed on the interaction, so as to
represent conventions, laws and suchlike; (2) it will allow committing to
complex conditions, which describe not only what will be achieved but to some
extent also how. These two aspects will be deeply investigated in the proposal
of a unified framework, which is part of the ongoing work and will be included
in the thesis.Comment: Proceedings of the Doctoral Consortium and Poster Session of the 5th
International Symposium on Rules (RuleML 2011@IJCAI), pages 1-8
(arXiv:1107.1686
Flocking with Obstacle Avoidance
In this paper, we provide a dynamic graph theoretical framework for flocking in presence of multiple obstacles. In particular, we give formal definitions of nets and flocks as spatially induced graphs. We provide models of nets and flocks and discuss the realization/embedding issues related to structural nets and flocks. This allows task representation and execution for a network of agents called alpha-agents. We also consider flocking in the presence of multiple obstacles. This task is achieved by introducing two other types of agents called beta-agents and gamma-agents. This framework enables us to address split/rejoin and squeezing maneuvers for nets/flocks of dynamic agents that communicate with each other. The problems arising from switching topology of these networks of mobile agents make the analysis and design of the decision-making protocols for such networks rather challenging. We provide simulation results that demonstrate the effectiveness of our theoretical and computational tools
Organisational Abstractions for the Analysis and Design of Multi-Agent Systems
The architecture of a multi-agent system can naturally be viewed as a computational organisation. For this reason, we believe organisational abstractions should play a central role in the analysis and design of such systems. To this end, the concepts of agent roles and role models are increasingly being used to specify and design multi-agent systems. However, this is not the full picture. In this paper we introduce three additional organisational concepts - organisational rules, organisational structures, and organisational patterns - that we believe are necessary for the complete specification of computational organisations. We view the introduction of these concepts as a step towards a comprehensive methodology for agent-oriented systems
Behavioral types in programming languages
A recent trend in programming language research is to use behav- ioral type theory to ensure various correctness properties of large- scale, communication-intensive systems. Behavioral types encompass concepts such as interfaces, communication protocols, contracts, and choreography. The successful application of behavioral types requires a solid understanding of several practical aspects, from their represen- tation in a concrete programming language, to their integration with other programming constructs such as methods and functions, to de- sign and monitoring methodologies that take behaviors into account. This survey provides an overview of the state of the art of these aspects, which we summarize as the pragmatics of behavioral types
A group learning management method for intelligent tutoring systems
In this paper we propose a group management specification and execution method that seeks a compromise between simple course design and complex adaptive group interaction. This is achieved through an authoring method that proposes predefined scenarios to the author. These scenarios already include complex learning interaction protocols in which student and group models use and update are automatically included. The method adopts ontologies to represent domain and student models, and object Petri nets to specify the group interaction protocols. During execution, the method is supported by a multi-agent architecture
Logic-Based Specification Languages for Intelligent Software Agents
The research field of Agent-Oriented Software Engineering (AOSE) aims to find
abstractions, languages, methodologies and toolkits for modeling, verifying,
validating and prototyping complex applications conceptualized as Multiagent
Systems (MASs). A very lively research sub-field studies how formal methods can
be used for AOSE. This paper presents a detailed survey of six logic-based
executable agent specification languages that have been chosen for their
potential to be integrated in our ARPEGGIO project, an open framework for
specifying and prototyping a MAS. The six languages are ConGoLog, Agent-0, the
IMPACT agent programming language, DyLog, Concurrent METATEM and Ehhf. For each
executable language, the logic foundations are described and an example of use
is shown. A comparison of the six languages and a survey of similar approaches
complete the paper, together with considerations of the advantages of using
logic-based languages in MAS modeling and prototyping.Comment: 67 pages, 1 table, 1 figure. Accepted for publication by the Journal
"Theory and Practice of Logic Programming", volume 4, Maurice Bruynooghe
Editor-in-Chie
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning
Using touch devices to navigate in virtual 3D environments such as computer
assisted design (CAD) models or geographical information systems (GIS) is
inherently difficult for humans, as the 3D operations have to be performed by
the user on a 2D touch surface. This ill-posed problem is classically solved
with a fixed and handcrafted interaction protocol, which must be learned by the
user. We propose to automatically learn a new interaction protocol allowing to
map a 2D user input to 3D actions in virtual environments using reinforcement
learning (RL). A fundamental problem of RL methods is the vast amount of
interactions often required, which are difficult to come by when humans are
involved. To overcome this limitation, we make use of two collaborative agents.
The first agent models the human by learning to perform the 2D finger
trajectories. The second agent acts as the interaction protocol, interpreting
and translating to 3D operations the 2D finger trajectories from the first
agent. We restrict the learned 2D trajectories to be similar to a training set
of collected human gestures by first performing state representation learning,
prior to reinforcement learning. This state representation learning is
addressed by projecting the gestures into a latent space learned by a
variational auto encoder (VAE).Comment: 17 pages, 8 figures. Accepted at The European Conference on Machine
Learning and Principles and Practice of Knowledge Discovery in Databases 2019
(ECMLPKDD 2019
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